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Modelling of dewatering wood pulp in a screw press using statistical and multivariate analysis

Bioresources, 2020-08, Vol.15 (3), p.5899-5912 [Peer Reviewed Journal]

2020. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms available at https://bioresources.cnr.ncsu.edu/about-the-journal/editorial-policies ;ISSN: 1930-2126 ;EISSN: 1930-2126 ;DOI: 10.15376/biores.15.3.5899-5912

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  • Title:
    Modelling of dewatering wood pulp in a screw press using statistical and multivariate analysis
  • Author: El Idrissi, Bouchaib ; Loranger, Éric ; Lanouette, Robert ; Bousquet, Jean Pierre ; Martinez, Mark
  • Subjects: Design of experiments ; Dewatering ; Energy consumption ; Experimental design ; Experiments ; Inlet pressure ; Mathematical models ; Multivariate analysis ; Parameters ; Performance evaluation ; Prediction models ; Pulp ; Software ; Statistical analysis ; Statistical models ; Wood pulp
  • Is Part Of: Bioresources, 2020-08, Vol.15 (3), p.5899-5912
  • Description: Statistical modeling of a screw press was established by using an experimental design based on the screw rotational speed, the pulp feed consistency, the pulp feed suspension freeness, the inlet pressure, and the counter-pressure at the discharge end. The statistical models showed that the screw press outputs for each pulp could be predicted. When including all data in a global model to predict the outputs of the press for any pulp, a global statistical model was found not to be efficient by using just the five fixed parameters. The solution to this problem was to use a multivariate analysis to include more parameters, mainly about the fiber characteristics (crowding factor, fiber length, fiber width, and fines content). By including these fiber properties, the differences between each pulp were more properly analyzed. The multivariate analysis predicted the press outsets very well in a global model by using eight parameters instead of five. The R2 values of the multivariate prediction model were all higher than 0.70 and had the goodness of prediction (Q2¬¬¬) higher than 0.60.
  • Publisher: Raleigh: North Carolina State University
  • Language: English
  • Identifier: ISSN: 1930-2126
    EISSN: 1930-2126
    DOI: 10.15376/biores.15.3.5899-5912
  • Source: GFMER Free Medical Journals
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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